Patent US6330484B1 successfully refreshed from Google Patents.
US6330484B1
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Method and apparatus for fuzzy logic control with automatic tuning
Assignee: Fisher Rosemount Systems Inc
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Filing: 1993-08-11
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Granted: 2001-12-11
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Expires: 2013-08-11
Abstract
Abstract A system and method for automatically tuning a fuzzy logic process controller is presented. A process under control is automatically assessed to determined process control characteristics including, for example, ultimate gain, ultimate period and time delay. Then, these quantities are used to calculate tunable control parameters which include scaling factors, which are applied to the fuzzy logic controller in order to optimize the performance of the fuzzy logic controller when connected to control the process.
Inventors
- 👤 S. Joe Qin
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-31 10:07
CPC Classifications
G; G05; G05B; G05B13/00; G05B13/02; G05B13/0265; G05B13/0275; Y; Y10; Y10S; Y10S706/00; Y10S706/90
Claims Analysis
Total Claims: 17
Claim 1
Independent Claim
127 words
A method of automatically tuning a fuzzy logic controller used to control a process in a predetermined manner, said process producing at least one process variable and having at least one control action input connected to said fuzzy logic controller, said method comprising: determining from said process a plurality of dynamic process characteristics for tuning a proportional integral derivative controller; calculating fuzzy control parameters as functions of said dynamic process characteristics for tuning said fuzzy logic controller; and tuning said fuzzy logic controller using said fuzzy control parameters for controlling the process, said determining step comprising: causing said process to undergo controlled induced oscillation; and determining from said process said plurality of dynamic process characteristics for tuning a proportional integral derivative controller during oscillation of the process.
Claim 2
Independent Claim
127 words
A method of automatically tuning a fuzzy logic controller used to control a process in a predetermined manner, said process producing at least one process variable and having at least one control action input connected to said fuzzy logic controller, said method comprising: determining from said process a plurality of dynamic process characteristics for tuning a proportional integral derivative controller; calculating fuzzy control parameters as functions of said dynamic process characteristics for tuning said fuzzy logic controller; and tuning said fuzzy logic controller using said fuzzy control parameters for controlling the process, said determining step comprising: causing said process to undergo controlled induced oscillation; and determining from said process said plurality of dynamic process characteristics for tuning a proportional integral derivative controller during oscillation of the process.
Claim 3
Dependent Claim
45 words
The method of claim 1 , said causing step comprising: disconnecting said fuzzy logic controller from said at least one control action input; and connecting said at least one control input to a controllable signal generator to cause said process to undergo controlled induced oscillation.
Claim 4
Dependent Claim
25 words
The method of claim 2 , further comprising, reconnecting said fuzzy logic controller to said at least one control action input, after said tuning step.
Claim 5
Dependent Claim
35 words
The method of claim 1 , said causing step comprising, injecting an excitation signal into a closed loop including said fuzzy logic controller and said process to cause said closed loop to undergo induced oscillation.
Claim 6
Independent Claim
115 words
A method of automatically tuning a fuzzy logic controller used to control a process in a predetermined manner, said process producing at least one process variable and having at least one control action input connected to said fuzzy logic controller, said method comprising: determining from said process a plurality of dynamic process characteristics for tuning a proportional integral derivative controller; calculating fuzzy control parameters as functions of said dynamic process characteristics for tuning said fuzzy logic controller; and tuning said fuzzy logic controller using said fuzzy control parameters for controlling the process, said determining step comprising, determining said plurality of dynamic process characteristics for tuning a proportional integral derivative controller by a model matching method.
Claim 7
Independent Claim
115 words
A method of automatically tuning a fuzzy logic controller used to control a process in a predetermined manner, said process producing at least one process variable and having at least one control action input connected to said fuzzy logic controller, said method comprising: determining from said process a plurality of dynamic process characteristics for tuning a proportional integral derivative controller; calculating fuzzy control parameters as functions of said dynamic process characteristics for tuning said fuzzy logic controller; and tuning said fuzzy logic controller using said fuzzy control parameters for controlling the process, said determining step comprising, determining said plurality of dynamic process characteristics for tuning a proportional integral derivative controller by a model matching method.
Claim 8
Independent Claim
146 words
A method of automatically tuning a fuzzy logic controller used to control a process in a predetermined manner, said process producing at least one process variable and having at least one control action input connected to said fuzzy logic controller, said method comprising: determining from said process a plurality of dynamic process characteristics for tuning a proportional integral derivative controller; calculating fuzzy control parameters as functions of said dynamic process characteristics for tuning said fuzzy logic controller; and tuning said fuzzy logic controller using said fuzzy control parameters for controlling the process, said fuzzy control parameters including scaling factors for said fuzzy logic controller, said scaling factors including control error scaling factor, change in control error scaling factor, and change in control action scaling factor, said dynamic process characteristics for tuning a proportional integral derivative controller including critical gain and integral time constant of said process.
Claim 9
Independent Claim
146 words
A method of automatically tuning a fuzzy logic controller used to control a process in a predetermined manner, said process producing at least one process variable and having at least one control action input connected to said fuzzy logic controller, said method comprising: determining from said process a plurality of dynamic process characteristics for tuning a proportional integral derivative controller; calculating fuzzy control parameters as functions of said dynamic process characteristics for tuning said fuzzy logic controller; and tuning said fuzzy logic controller using said fuzzy control parameters for controlling the process, said fuzzy control parameters including scaling factors for said fuzzy logic controller, said scaling factors including control error scaling factor, change in control error scaling factor, and change in control action scaling factor, said dynamic process characteristics for tuning a proportional integral derivative controller including critical gain and integral time constant of said process.
Claim 10
Dependent Claim
133 words
The method of claim 6 , said dynamic process characteristics for tuning a proportional integral derivative controller and said fuzzy control parameters being related by the equations: K c = S Δ u S Δ e 4 ( 1 - 0.5 α ) T i = S e S Δ e Δ t where: K c is the critical gain of the process, T i is the integral time constant of the process, S Δu is the change in control action scaling factor, S Δe is the change in control error scaling factor, S e is the control error scaling factor, Δt is the sampling rate of the fuzzy logic controller, and α is a stability margin adjustment factor.
Claim 11
Dependent Claim
129 words
The method of claim 6 , said dynamic process characteristics for tuning a proportional integral derivative controller and said fuzzy control parameters being related by the equations: K c = S Δ u S Δ e ( 3 - α ) T i = S e S Δ e Δ t where: K c is the critical gain of the process, T i is the integral time constant of the process, S Δu is the change in control action scaling factor, S Δe is the change in control error scaling factor, S e is the control error scaling factor, Δt is the sampling rate of the fuzzy logic controller, and α is a stability margin adjustment factor.
Claim 12
Dependent Claim
128 words
An automatically tunable control system, comprising: a fuzzy logic controller having tunable fuzzy control parameters and being adapted for controlling a process; and a tuning module for determining dynamic process characteristics of said process for tuning a proportional integral derivative controller, for calculating fuzzy control parameters for said fuzzy logic controller as functions of said dynamic process characteristics, and for tuning said fuzzy logic controller using said fuzzy control parameters, said tuning module comprising: a controllable signal generator selectively connectable to said system for causing said system to undergo induced oscillation; and a tuner for selectively connecting said controllable signal generator to said system, for determining said dynamic process characteristics during induced oscillation, and for calculating said tunable fuzzy control parameters as functions of said dynamic process characteristics.
Claim 13
Dependent Claim
128 words
An automatically tunable control system, comprising: a fuzzy logic controller having tunable fuzzy control parameters and being adapted for controlling a process; and a tuning module for determining dynamic process characteristics of said process for tuning a proportional integral derivative controller, for calculating fuzzy control parameters for said fuzzy logic controller as functions of said dynamic process characteristics, and for tuning said fuzzy logic controller using said fuzzy control parameters, said tuning module comprising: a controllable signal generator selectively connectable to said system for causing said system to undergo induced oscillation; and a tuner for selectively connecting said controllable signal generator to said system, for determining said dynamic process characteristics during induced oscillation, and for calculating said tunable fuzzy control parameters as functions of said dynamic process characteristics.
Claim 14
Dependent Claim
30 words
The automatically tunable control system as recited in claim 9 , further comprising a switch for alternately connecting said controllable signal generator and said fuzzy logic controller to said system.
Claim 15
Dependent Claim
38 words
The automatically tunable control system as recited in claim 9 , further comprising a switch for alternately connecting and disconnecting said controllable signal generator to and from said system to selectively inject an excitation signal into said system.
Claim 16
Dependent Claim
78 words
An automatically tunable control system, comprising: a fuzzy logic controller having tunable fuzzy control parameters and being adapted for controlling a process; and a tuning module for determining dynamic process characteristics of said process for tuning a proportional integral derivative controller, for calculating fuzzy control parameters for said fuzzy logic controller as functions of said dynamic process characteristics, and for tuning said fuzzy logic controller using said fuzzy control parameters, said tuning module comprising a model matching tuner.
Claim 17
Dependent Claim
78 words
An automatically tunable control system, comprising: a fuzzy logic controller having tunable fuzzy control parameters and being adapted for controlling a process; and a tuning module for determining dynamic process characteristics of said process for tuning a proportional integral derivative controller, for calculating fuzzy control parameters for said fuzzy logic controller as functions of said dynamic process characteristics, and for tuning said fuzzy logic controller using said fuzzy control parameters, said tuning module comprising a model matching tuner.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2004-12-10 | 2005-06-11 | 2005-12-11 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2008-12-10 | 2009-06-11 | 2009-12-11 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2012-12-10 | 2013-06-11 | 2013-12-11 | $7700.00 | Window Closed / Lapsed |